Gender Differences in Drug Use, Sexually Transmitted Diseases,and Risky Sexual Behavior among Arrested Youths*

Richard Dembo,Department of Criminology, University of South Florida, Tampa, FL

Steven Belenko,Department of Criminal Justice, Temple University, Philadelphia, PA

Kristina Childs,Department of Psychology, University of New Orleans, New Orleans, LA

Paul E. Greenbaum, andDepartment of Child and Family Studies, University of South Florida, Tampa, FL

Jennifer WarehamDepartment of Criminal Justice, Wayne State University, Detroit, MI

AbstractData were collected on arrested youths processed at a centralized intake facility, including youthsreleased back to the community and those placed in secure detention. This paper reports the resultsof a test of a structural model involving newly arrested male and female youths’ sexuallytransmitted diseases (STD) test results, urine analysis results for recent cocaine and marijuana use,and self-reported engaging in risky sexual behavior. The across gender, multiple group modelinvolved: (1) a confirmatory factor analysis of these variables, reflecting a latent variable labeledRisk, (2) a regression of Risk on the youths’ age, and (3) an examination of the covariancebetween Risk and the youths’ race and seriousness of arrest charge. Results indicate the youths’STD status, drug use, and reported risky sexual behavior are interrelated phenomena, similarlyexperienced across gender. Age was the only correlate of Risk status that demonstrated asignificant gender group difference. The youths’ race and seriousness of arrest charges did notsignificantly affect Risk, regardless of gender. Research and policy implications of the findings arediscussed.

KeywordsJuvenile offenders; risky sexual behavior; substance use; sexually transmitted disease

In recent years, attention has been directed to the numerous health related risk-takingbehaviors engaged in by adolescents. In general, risk-taking refers to “participation inbehavior which involves potential negative consequences (or loss) balanced in some way byperceived positive consequences (or gain)” (Gullone & Moore, 2000:393). Some examplesof juvenile risk-taking behaviors include, but are not limited to, delinquent behavior,substance use, risky sexual practices, reckless driving, dangerous sports, poor eating habits,

*Preparation of this manuscript was supported by Grant # DA020346, funded by the National Institute on Drug Abuse. The authorsare grateful for their support. However, the research results reported and the views expressed in the paper do not necessarily imply anypolicy or research endorsement by our funding agency. We would also like to thank the Hillsborough County, FL Juvenile AssessmentCenter and the Hillsborough County Health Department. We are grateful for Dr. Bengt Muthén’s advice on the analyses for this paper.

NIH Public AccessAuthor ManuscriptJ Child Adolesc Subst Abuse. Author manuscript; available in PMC 2011 November 1.

Published in final edited form as:J Child Adolesc Subst Abuse. 2010 November 1; 19(5): 424–446. doi:10.1080/1067828X.2010.515886.

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truancy, and negative peer associations. Research indicates that adolescents areoverrepresented in a wide range of risky-taking behaviors (Arnett, 1992; Bonino, Cattelino,& Ciairano, 2003; DiClemente et al., 1996). In addition, adolescents who engage in oneform of risk-taking behavior are more likely to engage in other forms of risky behavior(Elliot, Huizinga, & Menard, 1989; Jessor & Jessor, 1977;Levine & Singer, 1988).

Studies also suggest that, although adolescents in the general population display high ratesof risk-taking, these types of behaviors are inflated among certain populations, particularlyjuvenile offenders (Castrucci & Martin, 2002). Two of the more common risk-takingbehaviors that have been found to be strongly associated with juvenile offending are riskysexual practices and substance use. In fact, the relationship between juvenile delinquency,risky sexual practices, and substance use has been consistently documented for well overthree decades (Jessor & Jessor, 1977; Kotchick, Shaffer, Forehand, & Miller, 2001; LeBlancand Bouthillier, 2003, Tolou-Shams, Brown, Gordon, & Fernandez, 2007). More recently,research has demonstrated that: 1) juvenile offenders are disproportionably more likely toreport risky sexual practices and sexually transmitted disease (STD) infection compared tonon-offenders (Crosby, DiClemente, Wingood, Rose, & Levine, 2003; Devine, Long, &Forehand, 1993; DiClemente, Lanier, Horan, & Lodico, 1991; Elliott & Morse, 1989; Shaferet al., 1993; Morris et al., 1995; Tolou-Sham et al., 2007), 2) juvenile offenders reporthigher frequencies and more serious forms of substance use compared to non-offenders(Elliott et al., 1989; Huizinga & Jakob-Chien, 1998; Office of Applied Studies, 2003, 2004),3) juvenile offenders report higher levels of sex while using drugs or alcohol compared tonon-offenders (Malow, Devieux, Jennings, Lucenko, & Kalichman, 2001; Otto-Salaj, Gore-Felton, McGarvey, & Canterbury, 2002), and 4) juvenile offenders who use substances aresubstantially more likely to report risky sexual practices and STD infection compared tonon-substance using juvenile offenders (Castrucci & Martin, 2002; Kingree, Braithwaite, &Woodring, 2000; Kingree & Phan, 2001; Malow et al., 2001; Rosengard et al., 2006).

Several sociodemographic characteristics have been shown to affect the link between riskysexual practices and substance use among youth. For example, adolescents who reporthigher frequencies (Elliot & Morse, 1989; Harwell, Trino, Rudy, Yorkman, & Gollub, 1999;Tolou-Shams et al., 2007) and more serious forms of delinquent behavior (Farrington, 1998;Huizinga, Loeber, Thornberry, & Cothern, 2000; Robertson & Levin, 1999; Timmermans,Van Lier, & Koot, 2007), particularly violent behavior, are more likely to engage inadditional risk behaviors, including substance use, risky sexual practices, and STD infection.

Race has been shown to be an important factor for understanding risky sexual practices andsubstance use among juvenile offenders. On one hand, African-American incarceratedadolescents are more likely to report risky sexual practices and test STD positive(Canterbury et al., 1995; DiClemente, 1991; Kahn et al., 2005; Lofy, Hofmann, Mosure,Fine, & Marrazzo, 2006; Mertz, Voigt, Hutchins, & Levine, 2002; Morris et al., 1995). Onthe other hand, white juvenile offenders typically report higher levels and more seriousforms of substance use (Kilpatrick et al., 2000; McClelland, Elkington, Teplin, & Abram,2004). Moreover, recent research has suggested that African-American females represent thehighest risk group for negative health related outcomes related to risk-taking behavior(CDC, 2008). Specifically, a handful of studies have documented disproportionately higherrates of several risk behaviors, including risky sexual practices and STD status, among thisdemographic subgroup (CDC, 2006; De Genna, Cornelius, & Cook, 2007; Halpern et al.,2004; Kahn et al., 2005; Lofy et al., 2006).

Furthermore, age is one of the most consistent predictors of a range of risk-taking behavior.On average, as youth progress through adolescence, the tendency to engage in multipleforms of risky behaviors increases. In regard to risky sexual practices and substance use

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among juvenile offenders, a wealth of studies has revealed a linear relationship between ageand the co-occurrence of risky sexual practices and substance use. That is, older juvenileoffenders are more likely to report higher levels of risky sexual practices and substance use,than younger offenders (Kingree et al., 2000; Morris, Baker, Valentine, & Pennisi, 1998;Shafer et al., 1993; Teplin, Mericle, McClelland, & Abram, 2003).

Research also suggests that the relationship between substance use and risky sexualpractices among juvenile offenders differs across gender (Kingree & Betz, 2003; Robertson,Thomas, St. Lawrence, & Pack, 2005; Teplin et al., 2003). For example, previous studieshave found that female juvenile offenders display higher levels of serious drug use (e.g.,cocaine use) (Belenko, Sprott, & Peterson, 2004; Neff & Waite, 2007; Teplin et al., 2003;Kim & Fendrich, 2002; Wei, Makkai, & McGregor, 2003), while male juvenile offendersdisplay higher levels of marijuana use (Barnes, Welte, & Hoffman, 2002; Belenko et al.,2004; Dembo, Wareham, & Schmeidler, 2007; Stevens et al., 2003; Teplin et al., 2003). Inregard to sexual behavior, gender differences in risky sexual practices are mixed. Femalejuvenile offenders, however, are substantially more likely to test STD positive (Canterburyet al., 1995; Joesef, Kahn, & Weinstock, 2006; Kingree et al., 2000; Mertz et al., 2002), thanmale offenders. Currently, differences in the associations between these behaviors acrossgender groups are not well understood. The majority of studies examining the relationshipbetween substance use and sexual/STD risk among justice-involved youth and the influenceof gender have either relied on gender exclusive samples (typically females) or examinedgender differences in risk by simply controlling for the effects of a gender indicator.

The primary purpose of the present study was to examine the covariation among riskysexual practices, STD status, and substance use among a sample of recently arrested juvenileoffenders and compare the association among these measures across gender groups. It isimportant to examine the covariation in risky sexual practices and substance use acrossgender groups, while at the same time, considering the influence of additional demographicfactors such as race, age, and arrest charge. Failing to do so could lead to inaccurategeneralizations about the nature of these associations among adolescent offenders.Furthermore, such an examination will aid in the improvement of juvenile justice preventionand intervention services by identifying the unique treatment needs of male and femalejuvenile offenders.

This study makes a unique contribution to the literature in two important ways. First, thisstudy relied on a large sample of recently arrest youth demonstrating various levels ofcriminal and juvenile justice system (JJS) involvement. Previous studies of the risky sexualbehavior-drug use relationship remain quite limited for the general juvenile justicepopulation, especially those under community supervision. The handful of studies that havebeen conducted involve small samples of arrested youths and/or youths placed in securedetention centers or juvenile correctional facilities (Kahn et al., 2005; Pack, DiClemente,Hook, & Oh, 2000; Teplin et al., 2005). These studies fail to include the majority of at-risk,criminally involved youths who are released to the community following arrest (79.1% ofyouths arrested in 2005 were released back into the community [Stahl, Finnegan, & Kang,2008]). Second, this study compares the relationship between risky sexual practices, STDstatus, and marijuana and cocaine use across gender groups. This information will further anunderstanding of the tendency for male and female juvenile offenders to engage in a varietyof risky-taking behaviors. As such, the present paper seeks to extend previous research byincluding: (1) all youths having contact with the JJS, especially those at the front end, (2)biological test data on both drug use and STDs, and (3) sufficiently large samples of maleand female arrested juveniles to conduct statistically informed analyses.

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The present study tested the structural model shown in Figure 1. First, as shown, it washypothesized that STD test status, cocaine use, marijuana use, and self-reported risky sexualbehavior (e.g., having sex while using alcohol or other drugs and having intercourse withoutusing a condom) would reflect a latent construct of “Risk.” Second, it was hypothesized thatthe factor model for Risk in Figure 1 would be similar across gender. Finally, it washypothesized that this latent construct of Risk would be directly, positively affected by theyouth’s age and covary with the youth’s race and the seriousness of arrest charges, and thatthese effects would vary across gender.

MethodsData Collection Procedure

Participants were 948 newly arrested juveniles processed at the Hillsborough County, FLJuvenile Assessment Center (HJAC) (a centralized intake facility) between June 19 andSeptember 30, 2006 for males (n = 506) and between June 19 and December 31, 2006 forfemales (n = 442). Since females represent approximately 25 percent of the HJACpopulation, they were over-sampled to yield sufficient power for gender-specific analyses.

A simple, effective, and successful collaborative effort involving the HJAC, the FloridaDepartment of Health (DOH), Hillsborough County Health Department (HCHD), and theFlorida Department of Juvenile Justice (DJJ) was established and implemented as part ofthis NIDA-funded project. Based on the first author’s experience at the HJAC (Dembo &Brown, 1994), and discussions with HJAC personnel, DOH testing laboratory staff, andHCHD administrators, a protocol was established involving three major steps (discussed inmore detail in Belenko et al., 2008). First, project trained HJAC assessors provided briefSTD pre-counseling to newly arrested juveniles. Second, HJAC assessors requested arrestedjuveniles, who were over the age of 11 and agreed to provide a urine sample for drug testing(part of the standard HJAC procedure), to consent to their urine specimens being split forChlamydia and gonorrhea testing.1 Last, communication-coordination was establishedbetween DOH laboratory staff and HCHD Disease Intervention Specialists (DIS), whichinvolved DOH lab staff informing DIS staff of STD positive youths who DIS staff wouldthen seek to locate and treat.

In documenting the feasibility of front-end juvenile justice STD testing, participation rateswere high across all three HJAC shifts (7AM-3PM [78%], 3PM-11PM [71%], and11PM-7AM [72%]). No significant differences were found in STD testing participation bygender, race, age, and post-HJAC placement.

MeasuresSocio-demographic measures—Information was collected on the youths’ age and raceat the time of entry in the HJAC. Age was operationalized as a continuous indicatorrepresenting the number of years old. Race was dichotomized as African American (codedas 1) and non-African American, mostly Caucasian or White (coded as 0).

Drug use results—Voluntarily provided urine specimens were collected during the arrestprocessing at the HJAC. At the testing lab, the split urine specimens (UA) were tested fordrugs using the EMIT (enzyme multiplied immunoassay technique) procedure. In line withthe guidelines of the American Correctional Association and the Institute for Behavior andHealth, Inc. (1991), the cutoff levels for a positive for each drug were: 50 ng/ml of urine for

1Under Florida law, youths 12 years of age or older are protected from disclosure to parents of STD test results, and do not needparental consent to receive an STD test.

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marijuana and 300 ng/ml of urine for cocaine. The surveillance window for these substancesis as follows: for marijuana, moderate users = 5 days, heavy users = 10 days, chronic users =20 days; for cocaine, any use = 96 hours.2 The marijuana and cocaine UA results weredichotomized (0 = negative, 1 = positive) for the analyses. (Although the urine specimenswere tested for amphetamines and opiates, the prevalence rates for these two drugs were toolow for statistical analyses [1.8% and 0.5%, respectively].)

Chlamydia and gonorrhea—A non-invasive, FDA-approved, urine-based nucleic acidtest, GenProbe APTIMA Combo 2 Assay, was used to test for Chlamydia trachomatis andNeisseria gonorrhea. The sensitivity of Gen-Probe’s test has been shown to be superior toculture and direct specimen tests. The sensitivity and specificity of the GenProbe urine-based test are 95.9 percent and 98.2 percent, respectively for Chlamydia and 97.8 percentand 98.9 percent, respectively for gonorrhea (Chacko, Barnes, Wiemann, & DiClemente,2004). For analyses purposes, a dichotomous variable was created representing positive(coded as 1) results for any STD (i.e., Chlamydia, gonorrhea, or both) or negative (coded as0) results for all STD tests.

Current charge level—In accordance with Florida State law, each youth brought to theHJAC on a delinquency charge must have a Detention Risk Assessment Instrument (DRAI)completed on him/her by trained HJAC personnel (Dembo & Brown, 1994). The DRAItakes into consideration the youth’s most serious current offense, other current offenses andpending charges, prior offense history, current legal status, and aggravating or mitigatingcircumstances. On the basis of this information, each youth is assigned a point score of riskpotential. Youths receiving a score of 7 or more on the DRAI are placed under thesupervision of the DJJ; they are assigned a DJJ case manager who monitors their case untilfinal court disposition. The validity of the DRAI has been demonstrated (Dembo et al.,1994). The current charge level variable used in analyses differentiates diversion eligibleyouths (0 = DRAI score 0 to 6 points) from youths whose scores place them under thesupervision of DJJ (1 = DRAI score 7 or more).

Risky sexual behaviors—During the HJAC intake process, youths were asked tocomplete an STD/HIV Risk Assessment Questionnaire. Overall, the female and male youthsreported low rates of STD/HIV risk behavior. Seven of the eleven STD/HIV risk behavioritems shown in Table 1 (items 1, 2, 3, 4, 8, 10, and 11) refer to personal, risky sexualbehaviors. These seven items were combined into a risky sexual behavior index. The indexhad a skewed score range (0= 73.4%, 1= 18.4%, 2= 7.6%, 3= 0.2%, and 4= 0.4%). Hence,we included the few cases with three or four reported risky sexual behaviors with youthsreporting two such behaviors. Since we suspected underreporting in item 4 (Have you had asexually transmitted disease? −5% of females and <1% of males), this item was included inthis summary measure, rather than as a separate index of STD history.

ResultsDescription of the Female and Male Youths

As Table 2 shows, over half of the male youths, and just under half of the female youths,were African-American. However, a greater proportion of White females (43%), comparedto White males (35%), were represented in the study. The gender groups were similar in age.More males, than females, were arrested on serious charges, leading to placement in securedetention or on non-secure home detention (i.e., home arrest).

2Urine was also tested for the presence of amphetamines and opiates. Due to the low prevalence rates for these drugs (1.8% and 0.5%,respectively), however, these were excluded from the present analyses.

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Offense History Comparison of the YouthsTable 3 presents offense history information, obtained from official records, for the maleand female youths in the study. As can be seen, the male youths, on average, are younger atfirst arrest, have a larger number of prior arrests, and have spent more days in a securefacility, than the females. In regard to specific offense category differences, the males have asignificantly higher rate of prior arrests in five of the seven listed offense categories.

It is important to stress again that the HJAC is a central intake facility. As such, HJACprocesses first time offenders, as well as youths with previous arrests and incarcerationexperience. Depending on their score on the DRAI, HJAC processed youths are released tothe community for assignment to one or more diversion program, placed on home arrest, ortransported to a secure detention center.

STD, Drug Test Result, and Risky sexual behavior Comparison by GenderAs Table 4 shows, the males had higher, tested prevalence rates for marijuana, than thefemales. On the other hand, the females tended to report engaging in more risky sexualbehavior, and had higher STD prevalence rates, than the males.

Correlations among the Variables in the ModelTable 5 displays the correlations among the variables in the structural model for the maleand female youths. As can be seen, positive, significant relationships exist among the fourindicators of Risk for the female (overall, 15 of the 21 relationships are statisticallysignificant) and male (overall, 16 of the 21 relationships are statistically significant) youths.Among the females, significant relationships exist between: (1) cocaine use and marijuanause and between cocaine use and engaging in risky sexual behavior, and (2) betweenmarijuana use and engaging in risky sexual behavior. Among the males, significantrelationships were found between each pair of the indicators of Risk.

In regard to the socio-demographic variables, age is positively related to each of the fourindicators of Risk for the males, and to three of the indicators of Risk for the females.African-American males and females have significantly higher STD positive rates than theother youths in the study. In contrast, African-American youths have significantly lower UApositive rates for cocaine and marijuana (females only), and they are significantly less likelyto report risky sexual behavior, than the other youths.

Confirmatory Factor AnalysesThe confirmatory factor analyses (CFAs) were completed using Mplus version 5.1 (Muthén& Muthén, 2007). A chi-square test is used to test the fit of the models to the data, with lackof significance indicating an acceptable model fit. Mplus also provides a number ofdescriptive fit measures to assess the closeness of fit of the model to the data. Four fitindices were used to evaluate the model fit, using the following criteria as indicating anadequate fit: (1) the Tucker-Lewis coefficient (TLI: Tucker & Lewis, 1973), (2) thecomparative fit index (CFI: Bentler, 1990), (3) root mean square error of approximation(RMSEA: Steiger & Lind, 1980), and (4) the weighted root mean square residual (WRMR).The typical range for both TLI and CFI is between 0 and 1 (although the TLI can take avalue slightly greater than 1) with values greater than .90 indicating an acceptable fit(Arbuckle & Wothke, 1999; Browne & Cudek, 1993). For RMSEA, values at .05 or lessindicate a close model fit, and values between .05 and .08 indicate an adequate model fit(Browne & Cudek, 1993). WRMR values of less than .90 indicate a good model fit (Yu &Muthen, 2001).

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Our confirmatory factor analyses (CFAs) proceeded in several stages (Widaman & Reise,1997). First, we estimated the CFA involving one factor, including the three binary variables(STD test results, marijuana urine analysis test results, and cocaine urine analysis results)and the ordered polytomous variable for risky sexual behavior for the sample as a whole.The results indicated a good fit of the model to the data (χ2 [2, N = 948] = 1.32, p = 0.52;CFI = 1.000; TLI = 1.015; RMSEA = 0.000; WRMR = 0.309). Each of the observedvariables loaded significantly on the latent factor.

Next, we examined whether the factor structure was consistent across the female and malegroups. These analyses were conducted in two steps: (1) estimation of an unconstrainedCFA across the two gender groups, in which the factor loadings and thresholds were free tovary across the groups and the intercepts were held at zero; and (2) estimation of a strongmeasurement invariant model (Widaman & Reise, 1997) (i.e., a constrained CFA involvingequal factor loadings and thresholds across the two gender groups), with completion of achi-square difference test to assess if the restricted model significantly reduced the fit of themodel to the data. Results indicated: (a) a good fit of the unconstrained model to the data (χ2

[4, N = 948] = 1.56, p = 0.82; CFI = 1.000; TLI = 1.050; RMSEA = 0.000; WRMR = 0.336),(b) a good fit of the constrained model to the data (χ2 [7, N = 948] = 7.90, p = 0.34; CFI =0.993; TLI = 0.989; RMSEA = 0.017; WRMR = 0.780). (c) the chi-square difference testindicated that the constrained model did not significantly reduce the fit of the model to thedata (χ2 [3, N = 948] = 6.00, p = 0.11). That is, the good fit of the freely-estimated model didnot deteriorate when the assumption of equal loadings and thresholds across gender wasimposed.(Due to space concerns, tables reporting these results have not been presented.Copies are available from the senior author upon request.)

SEM Analyses and ResultsAs noted earlier, a structural model (see Figure 1) was estimated across the male and femaleyouths in the study. To test the hypothesis of no gender difference in the factor model forrisky behavior, the factor loadings and thresholds were constrained to be equal(measurement invariance) across the gender groups; and the model was estimatedsimultaneously using multi-group analysis. Even though significant gender differences inprevalence for STDs and marijuana test results were found (see Table 2), a measurementinvariance factor model was hypothesized to exist across the two groups involving theirSTD test results, UA test results for cocaine and marijuana, and self-reported risky sexualbehavior. This factor was posited to reflect an underlying latent variable, Risk. UA testresults for cocaine were used as the reference indicator in the factor analysis part of themodel.

In addition to holding the thresholds and factor loadings equal across the gender groups, theresidual variances of the factor indicators were freely estimated across the groups. Allstructural parameters (factor means, variances, covariances [i.e., Risk and race, and Risk andseriousness of current charge] and regression coefficients [i.e., regression of Risk on age])were free and not constrained to be equal across the groups. The factor means were fixed atzero for the first reference group (i.e., females) and free to be estimated for the males(Muthén and Muthén, 2007). The structural model was estimated using Mplus version 5.1(Muthén & Muthén, 2007). Since the STD and drug test results were binary variables, andreported risky sexual behavior was an ordered polytomous variable, a robust weighted leastsquares estimator with mean-adjusted and variance-adjusted chi-square test statistics(WLSMV), recommended by Muthén and Muthén (2007), was used in the analysis.

As Table 6 shows, the model fit the data well. The results indicate a non-significant chi-square value, good CFI and TLI values, a zero RMSEA, and an WRMR value below 0.90.The confirmatory factor analysis (CFA) part of the model, specifying equal factor loadings

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for the indicators of Risk across the gender groups, is consistent with the data. Age is asignificant, positive predictor of Risk among the female, but not the male, youths. Further,there are no significant covariances between Risk and race, or between Risk and seriousnessof current charge, for either gender group. For the female youths, the residual variance forthe latent variable Risk indicates a significant amount of the variance is not accounted for bythe variables in the CFA part of the model. On the other hand, for the male youths, 85percent of the variance in Risk is accounted for by the variables in the factor analysis.Finally, comparison of the Risk intercepts, where female Risk intercept has been fixed tozero and the male Risk intercept freely estimated, indicates the male intercept issignificantly different from the female mean of 0.00.

DiscussionThe primary goal of this study was to examine the covariation among several substance useand sexual behavior risk-taking indicators across gender groups in a sample of newlyarrested juvenile offenders. The results highlighted a common tendency for juvenileoffenders to engage in risky sexual practices and substance use. Consistent with the firsthypothesis, cocaine use, marijuana use, testing positive for an STD, and reportedengagement in risky sexual behavior form a latent construct of “Risk” that reflected the datawell. By using a sample of newly arrested juveniles that included first time offenders as wellas more serious, chronic offenders, this finding strengthens the evidence in support of ageneral disposition towards risk-taking behaviors among adolescent offenders as a whole.

Further, in support of our second hypothesis, the results of our study suggest that thecovariation among substance use and sexual risk behaviors is similar for boys and girlshaving contact with the juvenile justice system. That is, the latent variable of Risk possesseda similar factor structure for both the male and female juvenile offenders. This finding isconsistent with previous studies on general adolescent samples (Gillmore et al., 1991;Donovan & Jessor, 1985: Welte, Barnes, & Hoffman, 2004), as well as justice involvedadolescents (Dembo et al., 1992), that have failed to find any significant differences betweenboys and girls in the construct of deviance.

Partial support was found for the third hypothesis that there would be gender differences inthe relationships between Risk and age, race, and offense seriousness. Race and seriousnessof offense charge were not significant predictors of Risk for either of the groups. Using asample of justice-involved youth, Dembo et al. (1992) also failed to find significant racialdifferences in a latent construct of deviance; however, a number of studies based oncommunity samples of adolescents have revealed significant racial differences in the factorstructure of deviance (Bartlett, Holditch-Davis, & Belyea, 2005; Basen-Enquist,Edmundson, & Parcel, 1996; Welte et al., 2004). These findings suggest that, althoughimportant racial differences exist in the tendency for adolescents in general to engage inmultiple forms of risk-taking behaviors, these differences may not exist among youthsinvolved in the justice system.

The results of our study also suggest that older girls had a higher level of Risk, than youngergirls. It is somewhat surprising that no age effect was found on Risk for the male youths.This is somewhat contradictory to previous studies examining this issue. In general, studiesindicate problem behavior among male juvenile offenders increases with age and peaks inmid to late adolescence (Teplin et al., 2003).

The inconsistent finding regarding the association between age and Risk for the male youthsmay be related to the measures used in our study compared to earlier studies. Previousresearch highlighting age differences in the tendency to engage in multiple forms of risk-

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taking behavior among juvenile offenders rely on self-reported behaviors, particularlysubstance use (Kingree & Phan, 2001; LeBlanc & Girard, 1997; Teplin et al., 2003). Thecurrent study, on the other hand, relies on drug test urinalysis as a measure of substance usewhich results in a relatively short surveillance window for use. (For heavy users, marijuanastays in a youth’s system for approximately 20 days, and cocaine remains in the system forless than four days [Dembo et al., 1999]). Hence, the substance use measures included in ourstudy were only able to capture recent or current drug use, which limited the number of drugusers identified in the sample. The risky sexual behavior measures were based on self-reports. However, since these questions were asked by an intake screener who did not have apersonal relationship with the youth answering the questions, underreporting of thesebehaviors at the assessment center is quite likely. Additional research, involving both self-report and biological data on drug use, as well as additional risk factors (e.g., family andpeer influences), is needed to tease out the inconsistencies in the findings of our studycompared to previous research.

Another possible explanation for the nonsignificant association between age and Riskamong the male youths is that, for a large number of these youths, the disposition towardsdeviance may have developed prior to their contact with the justice system. That is, for theboys, the tendency to engage in risk-taking behaviors may have developed during mid to latechildhood and remained stable throughout adolescence. Research indicates that the age ofonset of deviant behavior occurs earlier among males, than females (D’Unger, Land, &McCall, 2002; Mazzerrolle, Brame, Paternoster, Piquero, & Dean, 2006; Moffit, 1994;Silverthorn & Frick, 1999; Van Lier, Wanner, Vitaro, 2007). This experience could explainwhy age was related to Risk for the females, but not the males. Additional research isneeded to clarify this issue.

Our results highlight the need for early, holistic prevention services that target an array ofrisk behaviors. It is well documented that adolescents who develop problem behavior at anearly age are more resistant to intervention and treatment in later years, and they persist inmore serious forms of problem behavior throughout the life span (Lipsey & Williams, 1998;Moffit, Caspi, Harrington, & Milne, 2002; Welsh, 2005). Prevention programs that impedethe development of risk-taking behavior offer a cost-effective solution to reducing theprevalence of serious and persistent deviance among both male and female adolescents.

Our findings also underscore the importance of routine screening for STDs, risky sexualbehavior, and drug use among youths who have contact with the justice system. A numberof interventions have been developed to reduce STD/HIV risk among juvenile offenders(Jemmott et al., 2000; McKernan McKay et al., 2004; St. Lawrence, Crosby, Belcker,Yazdani, & Brasfield, 1999). The impact of these STD/HIV interventions can be increasedby tailoring them to meet the needs of specific adolescent subgroups, such as AfricanAmerican females (DiClemente et al., 2008). The findings of this study suggest that juvenilejustice agencies should make the introduction of effective interventions that combine STD/HIV and substance use risk reduction a priority in their programs. Centralized intake centersprovide a great opportunity for the screening and assessment of a large, diverse number ofyouths. Intervention efforts targeting risk-taking behaviors at this phase of the juvenilejustice process are likely to be more effective than services at the back-end of the system,where a small portion of adolescents, who are typically the most serious offenders, end up.

There are two limitations to the study that should be mentioned. First, the data were cross-sectional. Hence, no causal statements about any of the relationships can be made. Second,the data were collected at one site. It would be important to replicate this study among frontend, juvenile justice youths in other jurisdictions serving diverse cultural groups, to, amongother things, assess the generalizability of the findings.

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Despite these limitations, the results of this study suggest the need for an urgent publichealth response to the high STD rates, as well as the drug use and risky sexual behaviorissues, presented by arrested juveniles. Strong public health and political commitments areneeded to address these serious public health problems among this highly vulnerablepopulation. A large number of youths processed by the JJS are from economically stressedfamilies who lack the resources to access health care (Dembo & Schmeidler, 2002). Thefront door of the juvenile justice system represents an important, procedurally efficient, andeffective opportunity to improve these youths’ health in a way that directly impacts thehealth of the general community.

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Figure 1.Structural Equation Model for Cocaine Use, Marijuana Use, STD, and Risky SexualBehavior with Covariates

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Tabl

e 1

Juve

nile

Ass

essm

ent C

ente

r Ris

k Q

uest

ions

% M

ales

Rep

ortin

g (n

= 5

00 o

r 50

1)%

Fem

ales

Rep

ortin

g (n

440

or

441)

Fish

er’s

Exa

ct T

est

1. H

ave

you

inje

cted

dru

gs?

1.2%

<1%

ns

2. H

ave

you

had

sex

whi

le u

sing

non

-inje

ctin

g dr

ugs,

incl

udin

g al

coho

l?7.

6%8.

6%ns

3. H

ave

you

trade

d se

x fo

r dru

gs o

r mon

ey?

<1%

<1%

ns

4. H

ave

you

had

a se

xual

ly tr

ansm

itted

dis

ease

?<1

%5.

0%**

*

5. A

re y

ou a

chi

ld o

f a w

oman

with

HIV

/AID

S?<1

%<1

%ns

6. A

re y

ou a

hem

ophi

liac?

<1%

<1%

ns

7. H

ave

you

had

a bl

ood

trans

fusi

on?

1.6%

<1%

ns

8. H

ave

you

had

inte

rcou

rse

with

the

oppo

site

sex

with

out u

sing

a c

ondo

m?

20.6

%24

.1%

ns

9. H

ave

you

been

sexu

ally

ass

aulte

d?<1

%10

.0%

***

10. H

ave

you

had

sexu

al in

terc

ours

e w

ith a

man

who

has

had

sex

with

a m

an?

<1%

<1%

ns

11. H

ave

you

had

sexu

al in

terc

ours

e w

ith a

per

son

at ri

sk fo

r HIV

/AID

S?1.

0%<1

%ns

Two

taile

d p-

valu

es:

* p <

.05;

**p

< .0

1;

*** p

< .0

01.

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Table 2

Sociodemographic Characteristics, Charge Level, Drug Use, and Post HJAC Placement by Gender

Race/Ethnicity: Male Female

White 34.7% 43.2%

African-American 54.0% 49.5%

Hispanic White 10.5% 7.2%

Hispanic Black 0.6% --

Other 0.2% --

100.0% 100.0%

(n = 504) (n = 442)

Fisher’s Exact Test (N = 946), p < .05

Age: Male Female

12 2.4% 3.8%

13 9.3% 9.7%

14 12.8% 15.8%

15 19.0% 20.6%

16 24.9% 24.0%

17 27.9% 21.9%

18 3.8% 4.1%

100.0% 100.0%

(n = 506) (n = 442)

χ2 (6, N = 948) = 6.96, p = n.s.

Charge Level: Male Female

Diversion 58.6% 71.9%

Dept. Juvenile Justice Case 41.4% 28.1%

100.0% 100.0%

(n = 505) (n = 442)

χ2 (1, N = 947) = 18.38, p < .001

Post HJAC Placement: Male Female

Diversion 55.2% 72.2%

Non-Secure Home Detention 18.0% 10.0%

Secure Detention 26.7% 17.9%

100.0% 100.0%

(n = 505) (n = 442)

χ2 (2, N = 947) = 29.63, p < .001

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Table 3

Arrest History and Secure Custody Information by Gender*

Males Females

Prior Arrest Information (s.d.)

Age at first arrest 13.88 (2.18) 14.33 (1.74)

F(1,942) = 11.75, p < .001

Number of prior arrests 2.61 (3.52) 1.37 (2.34)

F(1,943) = 39.53, p < .001

Number of prior arrests for:

Violent felonies 0.36 (0.88) 0.17 (0.49)

F(1,943) = 15.30, p < .001

Property felonies 0.59 (1.14) 0.14 (0.49)

F(1,942) = 57.50, p < .001

Drug felonies 0.07 (0.34) 0.01 (0.08)

F(1,942) = 14.10, p < .001

Violent misdemeanors 0.30 (0.70) 0.28 (0.69)

F(1,942) = 0.12, p = ns

Property misdemeanors 0.21 (0.50) 0.17 (0.45)

F(1,942) = 1.63, p = ns

Drug misdemeanors 0.16 (0.55) 0.04 (0.21)

F(1,942) = 18.33, p < .001

Public disorder misdemeanors 0.24 (0.65) 0.16 (0.50)

F(1,942) = 4.74, p < .05

Secure Custody Information (s.d.)

Number of days in secure detention: 14.98 (34.95) 5.75 (19.41)

F(1,933) = 23.93, p < .001

Number of days in secure custody (included detention) 37.37 (132.88) 11.75 (62.84)

F(1,933) = 13.53, p < .001

*Analyses reported in this table include 504 males and 441 females. One female was missing age of first arrest information.

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Table 4

Sexually Transmitted Disease, Drug Test Results, and HIV/STD Risk Behaviors by Gender

Male Female

Urine Analysis Drug Test Results

Marijuana:

Negative 57.0% 73.5%

Positive 43.0% 26.5%

100.0% 100.0%

(n = 505) (n = 441)

χ2(1, N = 946) = 27.86, p < .001

Cocaine:

Negative 94.1% 95.9%

Positive 5.9% 4.1%

100.0% 100.0%

(n=505) (n=441)

χ2(1, N = 946) = 1.69, p = n.s

Sexually Transmitted Diseases

Negative 89.3% 80.8%

Positive Chlamydia 7.7% 12.4%

Positive Gonorrhea 1.4% 2.5%

Positive Chlamydia and Gonorrhea 1.6% 4.3%

100.0% 100.0%

(n = 506) (n = 442)

χ2(3, N = 948) = 15.00, p < .01

Risky Sexual Behaviors

None 74.8% 71.8%

One 19.0% 17.8%

Two or more 6.2% 10.5%

(n = 500) (n = 439)

χ2(2, N = 939) = 5.71, p = .06

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Tabl

e 5

Tetra

chor

ic C

orre

latio

ns a

mon

g th

e D

epen

dent

and

Oth

er V

aria

bles

(mal

es a

bove

dia

gona

l, fe

mal

es b

elow

dia

gona

l)

Var

iabl

e1

23

45

67

1. S

TD--

.143

.156

.030

.276

***

.394

***

.337

***

2. C

ocai

ne.2

78*

--.5

16**

*.3

26**

.277

**−.217

*.2

77*

3. M

ariju

ana

.202

*.6

50**

*--

.267

***

.265

***

−.024

.148

*

4. R

isky

Sex

ual B

ehav

ior

.224

**.5

18**

*.2

77**

*--

.290

***

−.267

***

−.049

5. A

ge.1

55*

.256

.271

***

.331

***

--−.154

**.0

18

6. R

ace

(Afr

ican

Am

eric

an)

.396

***

−.332

*−.205

**−.228

**−.153

**--

.184

**

7. C

urre

nt C

harg

e (D

JJ c

ase)

.227

**.1

40.0

02−.025

−.110

.227

*--

Not

e: D

escr

iptiv

e in

form

atio

n on

thes

e va

riabl

es c

an b

e fo

und

in th

e na

rrat

ive.

Two-

taile

d p-

valu

es:

* p <

.05;

**p

< .0

1;

*** p

< .0

01.

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Tabl

e 6

Stru

ctur

al E

quat

ion

Mod

el fo

r STD

, Coc

aine

Use

and

Mar

ijuan

a U

se w

ith C

ovar

iate

s: E

stim

ated

for F

emal

es a

nd M

ales

Sep

arat

ely

Fem

ales

(n =

442

)M

ales

(n =

506

)

Est

imat

eS.

E.

Cri

tical

Rat

ioE

stim

ate

S.E

.C

ritic

al R

atio

Risk

by:

C

ocai

ne te

st re

sult

1.00

0--

--1.

000

----

M

ariju

ana

test

resu

lt0.

658

0.14

44.

58**

*0.

658

0.14

44.

58**

*

ST

D te

st re

sult

0.32

70.

099

3.30

***

0.32

70.

099

3.30

***

R

isky

sexu

al b

ehav

ior

0.63

20.

137

4.60

***

0.63

20.

137

4.60

***

Risk

on:

A

ge0.

293

0.07

04.

16**

*0.

151

0.09

41.

60

Risk

with

:

R

ace

(Afr

ican

Am

eric

an)

−0.105

0.58

1−0.18

0.00

20.

020

0.11

C

urre

nt c

harg

e (D

JJ c

ase)

0.01

60.

037

0.44

−0.001

0.03

3−0.03

Inte

rcep

t:

R

isk

0.00

60.

000

--2.

846

2.04

11.

39

Resi

dual

Var

ianc

es:

R

isk

0.78

70.

210

3.75

***

0.14

90.

185

0.81

Two

taile

d te

st p

-val

ues:

* p <

.10;

**p

< .0

5;

*** p

< .0

1; p

< .0

01

Mod

el fi

t sta

tistic

s: χ

2 (6

) = 3

.81,

p =

0.7

0; C

FI =

1.0

00; T

LI =

1.0

81; R

MSE

A =

0.0

00; W

RM

R =

0.7

55.

J Child Adolesc Subst Abuse. Author manuscript; available in PMC 2011 November 1.